Babelscape/multinerd

Repository for the paper "MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)" (NAACL 2022).

37
/ 100
Emerging

This project provides a massive, pre-annotated dataset of text in 10 languages, sourced from Wikipedia and WikiNews articles. It helps natural language processing (NLP) researchers and data scientists accurately identify and categorize specific entities like people, organizations, locations, diseases, or events within text. The output is structured data with identified entities, their classifications, and links to knowledge bases.

No commits in the last 6 months.

Use this if you need extensive, fine-grained, multilingual datasets to train or evaluate models for named entity recognition (NER) or entity linking across various languages and content types.

Not ideal if you require perfectly clean, human-annotated data, as this dataset is automatically generated and may contain some errors, especially for less frequent entity categories.

natural-language-processing data-science text-analysis multilingual-information-extraction machine-learning-datasets
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 13 / 25

How are scores calculated?

Stars

44

Forks

6

Language

Jupyter Notebook

License

Last pushed

Jan 30, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/Babelscape/multinerd"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.